Identifying Robust Correlates of Risk Preference: A Systematic Approach Using Specification Curve Analysis
Identifying Robust Correlates of Risk Preference: A Systematic Approach Using Specification Curve Analysis
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DOI：
10.1037/pspp0000287
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发表时间：
2021-02-01
影响因子：
7.6
通讯作者：
Mata, Rui
中科院分区：
文献类型：
--
作者：
Frey, Renato;Richter, David;Mata, Rui
人们的风险偏好被认为是许多现实生活中许多后续决策的核心，因此确定这一结构的稳健相关性非常重要。不同的心理学理论已经提出了一系列候选相关因素，但由于过去研究中对风险偏好的不同可操作性和分析限制，它们之间的相关性的强度和稳健性仍然不清楚。我们通过一项涉及风险偏好的几种可操作性的研究(所有这些都是从德国人口的不同样本中的每个参与者收集的；N=916)，并通过采用详尽的建模方法-规格曲线分析来解决这些问题。我们对6个候选相关因素(家庭收入、性别、年龄、流动智力、结晶智力、教育年限)的分析表明，性别和年龄与风险偏好有强烈和一致的关联，而其他候选关联显示出更弱和更(领域)特定的关联(除了结晶智力，没有强烈的关联)。结果进一步证明了结构可操作性在评估人们的风险偏好时的重要作用：自我报告的倾向测量发现了与所提出的相关因素的各种关联，但(激励的)行为测量基本上没有做到这一点。简而言之，6个候选相关性和风险偏好之间的关联主要取决于风险偏好是如何衡量的，而不是模型规范中是否包括以及哪些控制变量包括在内。目前的发现为提出风险偏好的候选相关性的几种理论提供了信息，并说明了人格研究如何受益于穷举建模技术来改进基本结构的理论和测量。
People's risk preferences are thought to be central to many consequential real-life decisions, malting it important to identify robust correlates of this construct. Various psychological theories have put forth a series of candidate correlates, yet the strength and robustness of their associations remain unclear because of disparate operationalizations of risk preference and analytic limitations in past research. We addressed these issues with a study involving several operationalizations of risk preference (all collected from each participant in a diverse sample of the German population; N = 916), and by adopting an exhaustive modeling approach-specification curve analysis. Our analyses of 6 candidate correlates (household income, sex, age, fluid intelligence, crystallized intelligence, years of education) suggest that sex and age have robust and consistent associations with risk preference, whereas the other candidate correlates show weaker and more (domain-) specific associations (except for crystallized intelligence, for which there were no robust associations). The results further demonstrate the important role of construct operationalization when assessing people's risk preferences: Self-reported propensity measures picked up various associations with the proposed correlates, but (incentivized) behavioral measures largely failed to do so. In short, the associations between the 6 candidate correlates and risk preference depend mostly on how risk preference is measured, rather than whether and which control variables are included in the model specifications. The present findings inform several theories that have suggested candidate correlates of risk preference, and illustrate how personality research may profit from exhaustive modeling techniques to improve theory and measurement of essential constructs.